Finding Optimal Algorithmic Parameters Using Derivative-Free Optimization

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Automated Algorithm Configuration and Parameter Tuning (2012, methodology): “Prominent and relatively recent methods that appear to be particularly suitable in this context are the covariance matrix adaptation evolution strategy (CMA-ES) by Hansen and Ostermeier (2001) and the mesh adaptive direct search (MADS) algorithms by Audet and Orban (2006).”
The irace package: Iterated racing for automatic algorithm configuration (2016, methodology): “In conﬁguration scenarios where all parameters are numerical, conﬁguration approaches may rely on the application of classical black-box numerical optimizers, such as CMA-ES [38] , BOBYQA [79] , or MADS [5] .”
Instance-Specific Algorithm Configuration (2014, methodology): “For derivative-free optimization of continuous variables, [11] introduced a mesh adaptive direct search (MADS) algorithm.”
Automated Tuning of Optimization Software Parameters (2007, background): “Audet and Orban (2006) provide a more general framework for parameter tuning, using mesh adaptive direct search to search through the space of settings.”
A particle swarm pattern search method for bound constrained global optimization (2007, background): “Within the pattern search framework, the use of the search step for surrogate optimization [5,30] or global optimization [1] is an active area of research.”